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This investigation presents an analytical model optimized for predicting the curve of Flexible Printed Circuits (FPCs), aiming to enhance design precision within the burgeoning realm of flexible electronics. By modifying the bending Mises stresses derived from the finite element model (FEM), an almost linear relationship was established between the deformed FPC curvature and its length. The analytical model, based on the trend of the FPC curve, integrates differential equations, the Jacobian matrix, and an iterative process. The model's performance, marked by its remarkable efficiency, is rigorously compared with Finite Element Method simulations. The results show a negligible error between the two methods, with the predicted results for different types of FPC curves proving to be highly accurate. The study's insights advocate for the model's adoption in practical applications, suggesting a paradigm shift towards more agile and efficient design methodologies in flexible electronics engineering.

Abstract This manuscript offers an exhaustive analysis of Flexible Printed Circuits (FPCs), concentrating on enhancing their design to surmount two primary challenges. Firstly, it seeks to obviate contact with proximate components. Secondly, it aspires to adhere to pre-established curvature constraints. Predicated on the curvature properties of FPCs, we have developed a model adept at accurately forecasting FPC deformation under diverse conditions. Our inquiry entails a thorough examination of various FPC configurations, including bell, 'U', and 'S' shapes. Central to our methodology is the strategic optimization of FPC spatial arrangements, aiming to avert mechanical interference and control curvature, thus mitigating mechanical strain. This dual-faceted strategy is pivotal in enhancing the durability and operational reliability of FPCs, particularly in contexts demanding elevated flexibility and precision. Our research offers essential insights into the refinement of FPC design, skillfully addressing the complexities associated with curvature and physical interaction. Collectively, this study advocates a comprehensive framework for the design and implementation of FPCs, significantly advancing the field of contemporary electronics by ensuring these components meet the evolving demands of the industry.

Abstract Grinding, a critical precision machining process for difficult-to-cut alloys, has undergone continual technological advancements to improve machining efficiency. However, the sustainability of this process is gaining heightened attention due to significant challenges associated with the substantial specific grinding energy and the extensive heat generated when working with difficult-to-cut alloys, renowned for their exceptional physical and mechanical properties. In response to these challenges, the widespread application of massive coolant in manufacturing industries to dissipate grinding heat has led to complex post-cleaning and disposal processes. This, in turn, has resulted in issues such as large energy consumption, a considerable carbon footprint, and concerns related to worker health and safety, which have become the main factors that restrict the development of grinding technology. This paper provides a holistic review of sustainability in grinding difficult-to-cut alloys, encompassing current trends and future directions. The examination extends to developing grinding technologies explicitly tailored for these alloys, comprehensively evaluating their sustainability performance. Additionally, the exploration delves into innovative sustainable technologies, such as heat pipe/oscillating heat pipe grinding wheels, minimum quantity lubrication, cryogenic cooling, and others. These groundbreaking technologies aim to reduce dependence on hazardous coolants, minimizing energy and resource consumption and carbon emissions associated with coolant-related or subsequent disposal processes. The essence of these technologies lies in their potential to revolutionize traditional grinding practices, presenting environmentally friendly alternatives. Finally, future development trends and research directions are put forward to pursue the current limitation of sustainable grinding for difficult-to-cut alloys. This paper can guide future research and development efforts toward more environmentally friendly grinding operations by understanding the current state of sustainable grinding and identifying emerging trends.

In this investigation, the asperities on the print surface have been reduced using a laser polishing technique. The improvement in mechanical properties (tensile and flexural strength), roughness of printed surface, and scan time of three-dimensional (3D)-printed new acrylonitrile-butadiene-styrene (ABS) polymers are among the industrial issues that the projected laser polishing process is explicitly intended to address. Response surface approach was used in a series of tests to determine how altering the laser's parameters influenced the test materials' top finish and the mechanical properties. The pre-polishing workpiece was compared in terms of mechanical characteristics and surface examination using a scanning electron microscope. According to the results, surface roughness was decreased by more than 18.85% for ABS utilizing laser polishing, from 8.01 to 6.50 μm. The sample's tensile strength improved by 8.1%, from 35.8 to 38.7 MPa. The samples' laser scan time was 0.143 min and the flexural strength increased by 2.58% from 58.01 to 59.51 MPa. These results demonstrate that a 3D-printed ABS polymer material's mechanical and surface qualities are enhanced by the novel post-processing laser polishing technique. In order to produce sustainable items, this innovative technique is quite helpful in the 3D printing sector.

The efficient arrangement of patterns on a substrate surface is of paramount importance across diverse domains, including manufacturing, design, nanotechnology, and printed electronics. This paper introduces three distinct strategies for pattern arrangement within the realm of two-dimensional cutting and packing challenges. Firstly, for irregular patterns, we have developed a modified genetic algorithm that effectively determines the spatial relationships between patterns, facilitating subsequent packing processes. Secondly, we present the “MaxiRectPack” method designed for rectangular patterns. This method utilizes partitioning techniques and a variety of orientation schemes to maximize pattern packing efficiency. Lastly, we propose a technique based on normalized 2-D cross-correlation for patterns of diverse shapes, allowing precise placement while accounting for variations in substrate and pattern energy. These methodologies are adaptable, catering to irregular, rectangular, and diverse pattern shapes, offering versatile solutions for pattern placement in the context of two-dimensional cutting and packing. With a strong emphasis on resource conservation and spatial efficiency, this research equips practitioners and researchers with practical tools for optimizing material utilization and pattern arrangement on substrates.

Traditionally, projects have been scheduled with a primary focus on optimizing economic bene-fits, often at the expense of environmental considerations. However, in today’s context, there is an increasing societal demand for organizations to incorporate sustainability practices into their project management policies. This research aims to address this pressing issue by developing a comprehensive mathematical model that takes into account two crucial objectives: project net present value (NPV) and greenhouse gas (GHG) emissions. This model introduces carbon emission constraints on a per-period basis, alongside the usual precedence and resource constraints. the problem represented by this model is proven to be NP-hard, which adds complexity to its solution. To tackle this challenge, two multi-objective metaheuristic algorithms, specifically the Modified Grey Wolf Optimizer (MGWO) and the Strength Pareto Evolutionary Algorithm 2 (SPEA2), have been employed to solve the model. Extensive comparative analyses of these me-ta heuristics across various multi-objective performance measures indicate that MGWO consistently outperforms SPEA2 in most scenarios. To illustrate the practical applicability of the pro-posed model, a real-world case study has been utilized. The results of this case study offer compelling insights, showing that project NPV can be significantly improved with only a marginal increase in GHG emissions. This finding highlights the model’s potential to provide pragmatic solutions and valuable trade-off options for project planners and practitioners striving to balance economic objectives with environmental sustainability.

This research introduces the Iterative Overlap Optimization Placement (IOOP) method, a novel approach designed to enhance the efficiency of irregular pattern packing by dynamically optimizing overlap ratios and pattern placements. Utilizing a modified genetic algorithm, IOOP addresses the complexities of arranging irregular patterns in a given space, focusing on improving spatial and material efficiency. This study demonstrates the method’s superiority over the traditional Size-First Non-Iterative Overlap Optimization Placement technique through comparative analysis, highlighting significant improvements in spatial utilization, flexibility, and material conservation. The effectiveness of IOOP is further validated by its robustness in handling diverse pattern groups and its adaptability in adjusting pattern placements iteratively. This research not only showcases the potential of IOOP in manufacturing and design processes requiring precise spatial planning but also opens avenues for its application across various industries, underscoring the need for further exploration into advanced technological integrations for tackling complex spatial optimization challenges.

The potential of Fused Filament Fabrication (FFF) technology to produce very complex geometrics has a profound impact on the physical world. Practitioners in the industry are developing predictive methods for assessing key parameters and responses of engineering materials. Enterprises are trying to solve problems such as Flexural Strength (FS), Average Surface Roughness (Ra), Tensile Strength (TS), sustainability including energy efficacy, and time management problems. This study's objective is divided into two main parts. The First part includes the parametric investigations of Polyethylene Terephthalate Glycol (PETG) polymer to optimize the mechanical properties, Ra, and sustainability, including Print Time (T) and Printer Energy consumption (E) of samples using the FFF technique. In second part, samples were printed at optimized parametric values and further post-processed to enhance the responses such as TS, FS, Ra, and Laser Scan Time (Tl) using CO2 laser polishing treatment. FFF with PETG, a biodegradable material, is commonly used in various fields, despite the traditionally significant surface roughness of FFF products. In pre-processing, by optimizing 3D printed parameters, FS of 69.9 MPa, lowest Ra of 6.8 μm, TS of 45.1 MPa, lowest T of 53 min and E of 0.20 kWh were attained. In post-processing, the Ra decreased by more than 58.38% due to laser polishing (6.8 μm–2.81 μm). The TS increased by 8.89%, from 45.1 MPa to 49.11 MPa. SEM investigation revealed that the gaps in the material lowered the FS by 5.09%, from 69.9 MPa to 66.34 MPa and Tl for 0.22 min. The fracture morphologies were studied as a means of learning more about the reinforcing process in the future. These outcomes prove that laser scanning can improve and alter the surface of an FFF product.

Electrical discharge machining (EDM) has emerged as a pivotal non-conventional production technique due to its unique capability to machine without the cutting tool’s physical contact with the workpiece, making it apt for brittle, delicate, and complex materials. This research delved into the influence of operational parameters—pulse duration (Ton), peak current (Ip), duty cycle (T), and gap voltage (Vg)—on machining attributes, namely material removal rate (MRR), electrode wear rate (EWR), and radial overcut (ROC) for AISI D2 steel. Utilizing the Taguchi L9 orthogonal array for experimental design, nine experiments were conducted, followed by signal-to-noise ratio (S/N ratio) computations. Key findings highlighted a 4.02 dB improvement in the S/N ratio for MRR, leading to a 29.13% improvement; a 10.35 dB enhancement in the S/N ratio for EWR, resulting in a 33.33% reduction; and a 2.20 dB increase in the S/N ratio for ROC, leading to a 28.57% increment. ANOVA analyses further underscored the predominant influence of all four parameters. The significance of these findings lies in optimizing the EDM process for increased efficiency, reduced tool wear, and enhanced precision, potentially leading to cost savings and improved production quality in industrial applications.

The biocompatible hydroxylapatite (Ca10(PO4)6(OH)2) (HA) coating is usually depositing on the titanium substrate to enhance the healing process of implant and to isolate it from the harsh body environment. However, poor mechanical properties of HA restrict its usage for orthopedic applications like hip and knee joints which have to bear the load. The biocompatibility is provided by HA and mechanical strength by the addition of reinforce with HA. Thermal post treatments have been mostly utilized to enhance the morphological and physicomechanical characteristics of as-deposition coatings. In the present research, the emphasis has been laid to analyze the outcome of thermal-processing on phases of hydroxyapatite in coatings developed by plasma spraying process. The surface-morphology of as-sprayed and thermally-processed, HA coated samples was studied using Scanning electron microscope (S.E.M.), Elemental-mapping, X-ray diffraction analysis (X.R.D.), and Fourier-transform infrared (F.T.-I.R.) spectroscopy techniques. The outcomes achieved from the X.R.D. analysis of the hydroxylapatite-coated samples after thermal-treatment reveal the formation of crystalline structure at 750 °C with some traces of CaO. Moreover, when the heat treatment was further done at 950 °C, the unwanted CaO was totally eliminated and coating with crystalline structure was obtained. No substrate element is visible in the X-ray mapping of coatings. The presence of ultrafine particles was observed on both substrates after the heat treatment of coatings at specified temperatures for 2hr in air. These ultrafine particles have the capability to bond with the bone and also helps for bone in-growth on their surfaces.

This research aims to establish the ideal settings for Nylon-6 (PA6) three-dimensional printing utilizing the fused filament production process and examine the resultant surface roughness. ANOVA, S/N ratio, and modeling are explained, along with their application in identifying the ideal values for surface roughness, sustainability, and mechanical properties. Average-surface roughness (Ra), root-mean-squared surface roughness (Rq), print time (PT), print energy (PE), and tensile testing (T) were explored as response parameters to identify the impact of PA6 parameters (layer thickness, extrusion temperature, print speed, and infill density). Tests of validity demonstrated a significant decline in Ra, Rq, PE, PT, and T for the ideal values of the developed product of 10.58 µm and 13.3 µm, 23 min, 0.13 kWh, and 42.7 Mpa, respectively. Ra, Rq, PT, PE, and T have all been optimized using Taguchi techniques as a preliminary step towards application in future research and prototypes.

The effective utilization of tribological characteristics of the mating parts is an important concern in the automobile industry. The primary intent of this work is to evaluate & enhance the tribology of a CrN coated disc (piston ring) as well as a cylindrical lining pin utilizing Al2O3 additives. The study mainly sought to significantly raise tribological characteristics by blending nano-particles with lubricating oils. In this work, the deposition of CrN coating takes place on the grey cast iron as a substrate with the help of ion plating which undeniably exhibits a strong adhesion of the layer to the substrate with improved structure and appearance. The wear behavior has been analyzed with the help of Pin on disc test wear tester at various operating conditions like speed load and lubrication. The friction and wear behavior has been analyzed with the nano additives Al2O3 along with Castrol 15W40 as lubricating oil. One of the novel optimization technique Response Surface Methodology (RSM) has been implemented for the analysis of various response parameters. The optimized value of wear rate is 0.01480 mm3/Nm at least sliding speed of 1.0 m/s and load of 80N for the nano additives lubrication. The recorded friction coefficient is 0.01140 at a load of 80N and speed 2 m/s under the influence of nano-particles in overall optimization. The wear mechanism of the CrN coating analyzed with the help of FE-SEM and EDS reveals the three body abrasion followed by erosion and particle pull-out. The increasing utilisation of Aluminum oxide (Al2O3) nano-particles as nano-additives have come to the realization that they could still aid prolong the lifespan performance of automotive parts.

White layer is commonly formed on the machined surface of nickel-based Inconel 718 superalloy, which probably induces the generation of cracks, leading to the fatigue life reduction of the workpieces. Nevertheless, the microstructure of the white layer is still not revealed clearly. The core issues, which this paper aims to reveal, are the transition of γ, γ′, γ'' and δ phases and dynamic recrystallization mechanism within the white layer formed during turning Inconel 718. Therefore, the dry orthogonal cutting tests with worn tools were conducted to make the white layer first. After that, the morphology and microstructure of the white layer are studied with the aid of scanning electron microscope (SEM), energy dispersive spectrometer (EDS), X-ray diffraction (XRD), and electron backscatter diffraction (EBSD). The results indicate that the thickness of the white layer is uneven and varies within three microns. Meanwhile, the amount of γ′ phase decreases, and δ phase is crushed and refined within the white layer. The lattice mismatch between γ and γ'' phases decreases and the white layer is more homogeneous than the original material. Furthermore, the grains were refined to submicron, and continuous dynamic recrystallization (CDRX) occurs in the white layer. The CDRX results in a recrystallized structure with significantly reduced defect density in the white layer formed during the turning of Inconel 718.

Aluminum matrix composites (AMCs), reinforced with SiC and Mo, are formulated using stir casting. The influence of SiC and Mo inclusions on microstructural behavior, and machinability of composites was investigated. Microstructural behavior is analyzed using scanning electron microscopy (SEM), X-ray diffraction (XRD) and energy dispersive spectroscopy (EDS) in the form of microhardness, tensile strength, wear behavior, density, and porosity as per ASTM standards. SEM and EDS confirm the existence and dispersion of SiC and Mo particulates in the matrix grain structure. XRD patterns indicate Al5Mo and Al12Mo compounds formation in hybrid composites, but with the least intensity. Optical microscopy imagery confirms dendritic structure formation with the inclusion of SiC and Mo particulates in the base matrix. Machined surface roughness was influenced by speed-feed while tool wear by all inputs.

Three-dimensional (3D) printing of polylactic acid using the fused filament fabrication approach is a widely used additive manufacturing method in various fields, despite the historical issue of substantial surface roughness in fused filament fabrication products. Several strategies have been utilized to minimize the surface roughness of 3D-printed items. However, laser polishing is a novel technique for reducing surface roughness and improving other material qualities. In this study, polylactic acid was examined using the laser polishing method for surface roughness and mechanical properties, such as tensile and flexural strength and laser scan time. Several trials were conducted to determine how changing the laser’s characteristics may affect the materials’ surface quality and mechanical qualities. Before the final test, preliminary tests were performed to determine the lowest potential heat-affected zone. Laser polishing reduced surface roughness by more than 88.8% (from 7.8 µm to 0.87 µm). The tensile strength of the specimen increased by 14.03%, from 39.2 MPa to 44.7 MPa. Polylactic acid had a constant flexural strength of 70.1 MPa before and after polishing, and the laser scan time for samples was 19.4 s. Polished morphologies were studied to learn more about the microstructure. These findings show that laser polishing can improve and modify the surface properties of a fused filament fabrication product, which can benefit the industry and researchers.

In this innovative work, Inconel 718 alloy turning simulation models under dry and cryogenic machining (Cryo) conditions are developed. The machinability characteristics of the aforementioned alloy were assessed with relation to cutting temperature (Tct) and cutting force (Fcf). The comparison of the Tct and Fcf results from simulation with those obtained under the identical experimental conditions served as additional evidence of the effectiveness of the suggested simulation model. By varying the cutting speed, the reduction in Tct under Cryo conditions was 9.36% to 11.98% compared to dry cutting. Regarding the force comparison under experiment and simulation, the average difference between the simulation and experimental values for the main cutting force (Fc) was 13.73%, whereas the average deviation for the feed force (Ff) was 14.63%. Response surface methodology (RSM) was employed to build the forecasting models for Tct and Fcf in cryogenic settings. These mathematical models showed excellent predictive performance and were able to estimate the Tct and Fcf under machining operations settings, according to the present research. When compared to dry cutting, Cryo reduced the cutting temperature, which had a positive impact on the alloy’s machinability.

Great biocompatibility and superior mechanical properties of titanium alloys (Ti6Al4V) stimulate the use of this material frequently in biomedical, defence, and aeronautical industries. In these applications, the end parts generally have complex curved profiles. Through conventional means of machining, complex profiles with high dimensional accuracy are not easily achievable due to difficult-to-cut nature of Ti6Al4V. Wire Electric Discharge Machining (WEDM) is a promising alternative to cut the complex features like angular and curved features. In this research, convex and concave profiles are machined in Ti6Al4V through WEDM. The influence of four machining parameters, namely servo voltage, wire feed, pulse On- and Off-time, over the geometrical accuracies of convex and concave profiles along with corner radii have been comprehensively investigated. The L27 orthogonal array was taken as the design of the experiment, and the results are evaluated in terms of statistical (ANOVA and signal-to-noise ratio) and microscopic morphology by Scanning Electron Microscopy (SEM). Optimized combination of machining parameters are sought capable of resulting in minimum geometric deviations (0.250% overcut in convex and 0.236% undercut in concave profiles) and corner radii of 0.106 mm. Moreover, the SEM analysis has confirmed that the discharge energy and erosion phenomenon significantly affect the profile accuracy as well as the surface integrity. In addition to optimized parameters the provision of wire offset, ranging within 0.169–0.173 mm, can further mitigate the geometric deviations of the actual machined profiles from the designed geometries.

The lower lead time and increasing demand for good quality parts require the industries to produce the parts manufactured sustainably with higher precision, improved productivity, and low cost using new techniques. Minimum quantity lubricant (MQL) is one of these techniques, which is economical and eco-friendly. This paper deals with the analysis and multi-response optimization of cutting process parameters of Aluminum alloy 6026-T9 in MQL and dry conditions using an integrated approach of composite desirability function and criteria importance through inter-criteria (CRITIC). The cutting parameters considered for the study are cutting speed, feed rate, depth of cut, and positive rank angle, while the responses are surface roughness profile (Ra, Rq, Rz), tool life and material removal rate. The type of insert used was a tungsten carbide, while the coolant was minimum quantity lubricant (MQL) vegetable oil (olive oil) performed at 150 mL/hr. Taguchi orthogonal array (L16) using sixteen experimental runs are designed for machining. Based on analysis of variance (ANOVA) it is identified that the most significant cutting process parameters for surface roughness profile (Ra, Rq, Rz) under both dry and MQL conditions are the feed rate, while for the tool life are cutting speed and feed rate and for material removal rate is cutting speed followed by depth of cut and feed rate. Under dry conditions the optimized levels obtained are cutting speed of 500 m/min, the feed rate of 0.3 mm/rev, depth of cut of 2 mm, and positive rake angle of 15°. However for MQL condition are cutting speed of 500 m/min, the feed rate of 0.4 mm/rev, depth of cut of 2 mm, and positive rake angle of 15°. Finally, the machined worked piece surface and inserts are morphologically characterized at different cutting parameters using scanning electron microscopy (SEM) and Energy Dispersive X-Ray Analysis (EDX).

This study aims to investigate the effects of process parameters feed, depth of cut and flow rate, on the temperature during face milling of the D2 tool steel under two different lubricant conditions, Minimum Quantity Lubrication (MQL) and Nanofluid Minimum Quantity Lubrication (NFMQL). Distilled water with the flow rate range 200-400 ml/hr was used in MQL. 2% by weight concentration of Al2O3 nanoparticles with distilled water as the base fluid used as NFMQL with same flow rate. Response surface methodology RSM central composite design CCD was used to design experiment run, modeling, and analysis. ANOVA was used for the adequacy and validation of the system. The comparison shows that NFMQL condition reduced more temperature during machining.

Industrial practitioners are working on predictive solutions for the precise evaluation of input parameters and processed surfaces of engineering materials. To aid the aeronautical industry, this study is an effort to develop the mathematical modelling for comprehensive surface analysis of input parameters and surface finish after dry machining of CLARM HBR, a steel alloy with attractive mechanical properties and wide applications in large caliber gun barrels and high-pressure vessels. Feed rate, rotational speed, and depth of cut were taken as quantitative parameters, whereas machining time was considered as a categorical factor with a classification of three levels. Response surface methodology (RSM) with a central component design has been used for the constitution of the experimental design, mathematical modelling, and analysis of developed models. Eighteen samples were prepared to perform the experimentation for the development of prediction models. The adequacy of the developed models was verified using analysis of variance (ANOVA), and the models were validated using confirmatory trial experiments, which revealed the experimental results agreeing with predictions. The feed rate was the most significant parameter in achieving the desired surface finish. An increase in rotational speed at a low feed rate resulted in very fine surface texture, as though it deteriorated the surface finish at higher feed rates. The superior surface quality obtained was 0.137 μm at parametric settings of 0.19 mm/rev feed, 90 rpm speed, 3 mm depth of cut, and 4 min time. Overall, higher values of surface roughness were frecorded in the third level of process variable time. The developed empirical models are expected to aid manufacturers and machining practitioners in the prediction of the desired surface finish concerning different parameters before the experimentations.

Distribution of temperature during the welding process is essential for predicting and realizing some important welding features such as microstructure of the welds, heat-affected zone (HAZ), residual stresses, and their effects. In this paper, a numerical model was developed using COMSOL Multiphysics of dissimilar laser welding (butt joint) of AISI 316L and Ti6Al4V thin sheet of 2.5 mm thickness. A continuous mode (CW) fiber laser heat source of 300 W laser power was used for the present study. A time-dependent prediction of temperature distributions was attempted. The heat source was assumed as a Hermit–Gaussian analytical function with a moving velocity of 120 mm/min. Both convective and radiant heat loss and phase change of the materials were considered for the analysis. In addition, variation of temperature-dependent material properties was also considered. The maximum and minimum temperature for the two materials at different times and the temperature in the different penetration depths were also predicted. It was found that the average temperature that can be achieved in the bottom-most surface near the weld line was more than 2400 K, which justifies the penetration. Averages of maximum temperatures on the weld line at different times at the laser spot irradiation were identified near 3000 K.The temperature fluctuation near the weld line was minimal and decreased more in the traverse direction. Scanning with a displaced laser relative to the interface toward the Ti6Al4V side reduces the maximum temperature at the interface and the HAZ of the 316L side. All of these predictions agree well with the experimental results reported in current literature studies.

Air-leakage detection is among the most important processes at the assembly stage of unclosed components, especially for large aircraft. A series of air-leakage detecting methods are generally applied during the final assembly, nevertheless, many of them are less effective to detect the leakage at the assembly stage. The present study aims to discuss the principles of ultrasonic generation in negative pressure conditions to detect the air-leakage. An ultrasonic-based detection method is proposed and designed to detect the air-leakage of unclosed components for aircraft. A relationship between the acoustic power, sound pressure, and the leak aperture detection distance was identified and discussed. A leakage rate model related to leakage rate, leak aperture, and system pressure was implemented and confirmed through experiments. Findings have indicated that the air-leakage can be detected effectively within a detection distance of 0.8 m and a leak aperture greater or equal to 0.4 mm with this method. Besides, the leak location, leak aperture, and leakage rate was acquired in an accurate and fast way. It is an effective method of detecting the air-leakage of unclosed components at the aircraft assembly stage reducing the testing time, energy consumption, and cost for the air-leakage detection in the final assembly stage of large aircraft.

The quest for advanced cooling/lubrication approaches for energy-efficient, eco-benign, and cost-effective sustainable machining processes is garnering attention in academia and industry. Electrical and embodied energy consumption plays an important role in reducing CO2 emissions. In the present study, new empirical models are proposed to assess sustainable indicators. The embodied energy, environmental burden, and cost of coolant/lubricant have been added in the proposed models. Initially, optimal levels of minimum quantity lubrication (MQL) oil flow rate, liquid LN2 flow rate, air pressure, and nanoparticle concentration were found. Based on optimal technological parameters, experiments were performed under the same cutting conditions (machining parameters) for MQL and cryogenic LN2-assisted external turning of Ti6-Al-4V titanium alloy. The electric power and energy consumption, production time/cost, and CO2 emissions were assessed for a unit cutting-tool life. Later, specific responses were measured and compared between both cooling and lubrication approaches. Results showed that hybrid Al-GnP nanofluid consumed 80.6% less specific cumulative energy and emitted 88.7% less total CO2 emissions. However, cryogenic LN2 extended tool life by nearly 70% and incurred 4.12% less specific costs with 11.1% better surface quality. In summary, after Energy–Economy–Ecology–Engineering technology (4E)-based analysis, cryogenic LN2 is sustainable economically but not environmentally and there is a need to improve the sustainable production of LN2 at an industrial scale to achieve environmental sustainability. The present study provides useful information to establish clean machining processes.

Sustainability assessments of cooling/lubrication-assisted advanced machining processes has been demanded by environment control agencies because it is an effective management tool for improving process sustainability. To achieve an effective and efficient sustainability evolution of machining processes, there is a need to develop a new method that can incorporate qualitative indicators to create a quantifiable value. In the present research work, a novel quantifiable sustainability value assessment method was proposed to provide performance quantification of the existing sustainability assessment methods. The proposed method consists of three steps: establishing sustainable guidelines and identifying new indicators, data acquisition, and developing an algorithm, which creates the Overall Performance Assessment Indicator (OPAI) from the sustainability assessment method. In the proposed algorithm, initially, both quantitative and qualitative sustainability indicators are normalized. After weight assignment and aggregation, the OPAI is obtained. The developed algorithm was validated from three literature case studies, and optimal cutting parameters were obtained. The present methodology provides effective guidelines for a machinist to enhance process performance and achieve process optimization. The study also offers a relationship between sustainable and machining metrics for the support of industrial sustainability.

Design and manufacturing are the key steps in the sustainable manufacturing of any product to be produced. Within the perspective of injection molds production, increased competitiveness and repeated changes in the design require a complete optimized manufacturing process. Local and minor improvements in the milling process do not generally lead to an optimized manufacturing process. The goal of the new geometry and parametric analysis of the mould is to reduce the quality issues in mild steel grade 60. In this explicit research, the surface roughness (smoothness) of indigenously produced injection moulds in the local market in Pakistan is investigated. The CNC milling machine (five-axis) is used for the manufacturing of an injection mould, and the Taguchi method of the design of the experiment is applied for parameters optimization. Hence, the overall process is assisted in balancing the milling machine parameters to trim down the surface roughness issue in mild steel moulds and increase their sustainability. The spindle speed (rpm), the depth of cut (mm), and the feed rate (mm/rev) are considered as input variables for process optimization, and the experiments are performed on mild steel grade 60. It is deduced that the combination of a spindle speed of 800 rpm, feed rate of 10 mm/rev and depth of cut of 0.5 mm is the best case in case of minimum surface roughness, which leads to sustainable products. It is also deduced from ANOVA, that the spindle speed is a factor that affects the surface roughness of mild steel products, while the feed rate turns out to be insignificant.

Carbon dioxide (CO2) laser cutting finds one of its most relevant applications in the processing of a wide variety of polymeric materials like thermoplastics and thermosetting plastics. Different types of polymeric materials like polypropylene (PP), polymethyl methacrylate (PMMA), low- and high-density polyethylene (LDPE, HDPE), are processed by laser for different household as well as commercial products in the industry. The reason is their easy availability and economical aspect in the market. The problems associated with laser cutting include heat-affected zone (HAZ) generated on the cut surface, kerf width (KW), surface roughness (SR), dross formation, and striations formation. Furthermore, other related problems include taper cutting for deep parts and high-power consumption. The primary purpose of this work is a comprehensive literature review in CO2 laser cutting of polymeric materials. The influence of parametric variation on the cut quality is also explained. Cut quality in terms of KW, SR, HAZ, dross formation, and striations formation is analyzed by optimizing cutting variables like laser power (PL), cutting speed (CS), assist gas pressure (Pg), pulse frequency, nozzle type and its diameter, and stand-off distance (SOD). The effects of the laser cutting on the properties of different thermoplastics/thermosetting materials are also reported. However, this topic requires further studies on exploring the range of polymeric materials, and their optimal parameters selection to improve the cut quality. Therefore, the research gaps and future research directions are also highlighted in the context of CO2 laser cutting for polymeric materials.

Recently, the application of nano-cutting fluids has gained much attention in the machining of nickel-based super alloys due their good lubricating/cooling properties including thermal conductivity, viscosity, and tribological characteristics. In this study, a set of turning experiments on new nickel-based alloy i.e., Inconel-800 alloy, was performed to explore the characteristics of different nano-cutting fluids (aluminum oxide (Al2O3), molybdenum disulfide (MoS2), and graphite) under minimum quantity lubrication (MQL) conditions. The performance of each nano-cutting fluid was deliberated in terms of machining characteristics such as surface roughness, cutting forces, and tool wear. Further, the data generated through experiments were statistically examined through Box Cox transformation, normal probability plots, and analysis of variance (ANOVA) tests. Then, an in-depth analysis of each process parameter was conducted through line plots and the results were compared with the existing literature. In the end, the composite desirability approach (CDA) was successfully implemented to determine the ideal machining parameters under different nano-cutting cooling conditions. The results demonstrate that the MoS2 and graphite-based nanofluids give promising results at high cutting speed values, but the overall performance of graphite-based nanofluids is better in terms of good lubrication and cooling properties. It is worth mentioning that the presence of small quantities of graphite in vegetable oil significantly improves the machining characteristics of Inconel-800 alloy as compared with the two other nanofluids.

The sugar mill roller shaft is one of the critical parts of the sugar industry. It requires careful manufacturing and testing in order to meet the stringent specification when it is used for applications under continuous fatigue and wear environments. For heavy industry, the manufacturing of such heavy parts (>600 mm diameter) is a challenge, owing to ease of occurrence of surface/subsurface cracks and inclusions that lead to the rejection of the final product. Therefore, the identification and continuous reduction of defects are inevitable tasks. If the defect activity is controlled, this offers the possibility to extend the component (sugar mill roller) life cycle and resistance to failure. The current study aims to explore the benefits of using ultrasonic testing (UT) to avoid the rejection of the shaft in heavy industry. This study performed a rigorous evaluation of defects through destructive and nondestructive quality checks in order to detect the causes and effects of rejection. The results gathered in this study depict macro-surface cracks and sub-surface microcracks. The results also found alumina and oxide type non-metallic inclusions, which led to surface/subsurface cracks and ultimately the rejection of the mill roller shaft. A root cause analysis (RCA) approach highlighted the refractory lining, the hot-top of the furnace and the ladle as significant causes of inclusions. The low-quality flux and refractory lining material of the furnace and the hot-top, which were possible causes of rejection, were replaced by standard materials with better quality, applied by their standardized procedure, to prevent this problem in future production. The feedback statistics, evaluated over more than one year, indicated that the rejection rate was reduced for defective production by up to 7.6%.

It is hypothesized that the orientation of tool maneuvering in the milling process defines the quality of machining. In that respect, here, the influence of different path strategies of the tool in face milling is investigated, and subsequently, the best strategy is identified following systematic optimization. The surface roughness, material removal rate and cutting time are considered as key responses, whereas the cutting speed, feed rate and depth of cut were considered as inputs (quantitative factors) beside the tool path strategy (qualitative factor) for the material Al 2024 with a torus end mill. The experimental plan, i.e., 27 runs were determined by using the Taguchi design approach. In addition, the analysis of variance is conducted to statistically identify the effects of parameters. The optimal values of process parameters have been evaluated based on Taguchi-grey relational analysis, and the reliability of this analysis has been verified with the confirmation test. It was found that the tool path strategy has a significant influence on the end outcomes of face milling. As such, the surface topography respective to different cutter path strategies and the optimal cutting strategy is discussed in detail.

Environmental protection is the major concern of any form of manufacturing industry today. As focus has shifted towards sustainable cooling strategies, minimum quantity lubrication (MQL) has proven its usefulness. The current survey intends to make the MQL strategy more effective while improving its performance. A Ranque–Hilsch vortex tube (RHVT) was implemented into the MQL process in order to enhance the performance of the manufacturing process. The RHVT is a device that allows for separating the hot and cold air within the compressed air flows that come tangentially into the vortex chamber through the inlet nozzles. Turning tests with a unique combination of cooling technique were performed on titanium (Grade 2), where the effectiveness of the RHVT was evaluated. The surface quality measurements, forces values, and tool wear were carefully investigated. A combination of analysis of variance (ANOVA) and evolutionary techniques (particle swarm optimization (PSO), bacteria foraging optimization (BFO), and teaching learning-based optimization (TLBO)) was brought into use in order to analyze the influence of the process parameters. In the end, an appropriate correlation between PSO, BFO, and TLBO was investigated. It was shown that RHVT improved the results by nearly 15% for all of the responses, while the TLBO technique was found to be the best optimization technique, with an average time of 1.09 s and a success rate of 90%.

Considering the significance of improving the energy efficiency, surface quality and material removal quantity of machining processes, the present study is conducted in the form of an experimental investigation and a multi-objective optimization. The experiments were conducted by face milling AISI 1045 steel on a Computer Numerical Controlled (CNC) milling machine using a carbide cutting tool. The Cu-nano-fluid, dispersed in distilled water, was impinged in small quantity cooling lubrication (SQCL) spray applied to the cutting zone. The data of surface roughness and active cutting energy were measured while the material removal rate was calculated. A multi-objective optimization was performed by the integration of the Taguchi method, Grey Relational Analysis (GRA), and the Non-Dominated Sorting Genetic Algorithm (NSGA-II). The optimum results calculated were a cutting speed of 1200 rev/min, a feed rate of 320 mm/min, a depth of cut of 0.5 mm, and a width of cut of 15 mm. It was also endowed with a 20.7% reduction in energy consumption. Furthermore, the use of SQCL promoted sustainable manufacturing. The novelty of the work is in reducing energy consumption under nano fluid assisted machining while paying adequate attention to material removal quantity and the product’s surface quality.

In the present study, the machinability indices of surface grinding of AISI D2 steel under dry, flood cooling, and minimum quantity lubrication (MQL) conditions are compared. The comparison was confined within three responses, namely, the surface quality, surface temperature, and normal force. For deeper insight, the surface topography of MQL-assisted ground surface was analyzed too. Furthermore, the statistical analysis of variance (ANOVA) was employed to extract the major influencing factors on the above-mentioned responses. Apart from this, the multi-objective optimization by Grey⁻Taguchi method was performed to suggest the best parameter settings for system-wide optimal performance. The central composite experimental design plan was adopted to orient the inputs wherein the inclusion of MQL flow rate as an input adds addition novelty to this study. The mathematical models were formulated using Response Surface Methodology (RSM). It was found that the developed models are statistically significant, with optimum conditions of depth of cut of 15 µm, table speed of 3 m/min, cutting speed 25 m/min, and MQL flow rate 250 mL/h. It was also found that MQL outperformed the dry as well as wet condition in surface grinding due to its effective penetration ability and improved heat dissipation property.